docstr: Args:
      annotators: set that can include pos, lemma, and ner.
      model: spaCy model to use (either path, or keyword like 'en').
docstr_tok: ["Args",":","annotators",":","set","that","can","include","pos",",","lemma",",","and","ner",".","model",":","spaCy","model","to","use","(","either","path",",","or","keyword","like","'en","'",")","."]
code: def __init__(self, **kwargs):
      """
      Args:
          annotators: set that can include pos, lemma, and ner.
          model: spaCy model to use (either path, or keyword like 'en').
      """
      model = kwargs.get("model", "en")
      self.annotators = copy.deepcopy(kwargs.get("annotators", set()))
      nlp_kwargs = {"parser": False}
      if not any(
          [p in self.annotators for p in ["lemma", "pos", "ner"]]
      ):
          nlp_kwargs["tagger"] = False
      if "ner" not in self.annotators:
          nlp_kwargs["entity"] = False
      self.nlp = spacy.load(model, **nlp_kwargs)
  
imports: import copy
  import logging
  import regex
  import spacy
label_desc: Use image node_id 1 to create a new SpacyTokenizer object from inherited base classes: Tokenizer with example: obj = SpacyTokenizer()